An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

• URL: http://arxiv.org/abs/2105.06442v1
• Date: Thu, 13 May 2021 17:33:28 GMT
• Title: An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings
• Authors: Hemank Lamba and Kit T. Rodolfa and Rayid Ghani
• Abstract summary: We investigate the performance of several methods that operate at different points in the machine learning pipeline across four real-world public policy and social good problems. We find a wide degree of variability and inconsistency in the ability of many of these methods to improve model fairness, but post-processing by choosing group-specific score thresholds consistently removes disparities.
• Score: 13.037143215464132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems. The machine learning research community has responded to this challenge with a wide array of proposed fairness-enhancing strategies for ML models, but despite the large number of methods that have been developed, little empirical work exists evaluating these methods in real-world settings. Here, we seek to fill this research gap by investigating the performance of several methods that operate at different points in the ML pipeline across four real-world public policy and social good problems. Across these problems, we find a wide degree of variability and inconsistency in the ability of many of these methods to improve model fairness, but post-processing by choosing group-specific score thresholds consistently removes disparities, with important implications for both the ML research community and practitioners deploying machine learning to inform consequential policy decisions.

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